One way of comparing a first image item to a second image item is to compute local features associated with each image item, and then compare the features of the first image item with the features of the second image item. If the first image item includes features that are close to the second image's features, then the first image item likely visually resembles the second image item.
The above approach can be used to retrieve information from a database of image items. In this application, a retrieval system extracts the features of a query image item and then finds the image items in the database that have a similar feature set. One problem with this approach is that it is it consumes a significant amount of time to generate and compare a large quantity of image features. This approach also requires a considerable amount of memory to store the computed features.
One way of addressing the above technical issues is to cluster groups of related features of a source dataset into respective “words,” to thereby form a vocabulary. Comparison of a query image item with the source dataset can then be performed on a word-level, rather than a more elementary feature-level. Nevertheless, prior approaches have not adequately explored the vocabulary-generating operation in suitable detail, resulting in potential inefficiencies and limitations in such approaches. For example, prior approaches generate a new vocabulary for each dataset to be searched, and there is a mindset that the vocabulary should be as big as possible.